I am currently working on the NetworkX open source project (work funded through a grant from Chan Zuckerberg Initiative!) Also collaborating with folks from the Scientific Python project (Berkeley Institute of Data Science), Anaconda Inc and GESIS, Germany. Before this I used to work on the GESIS notebooks and gesis.mybinder.org.
I am also interested in the development and maintenance of the open source data & science software ecosystem. I try to help around with the Scientific Open Source ecosystem wherever possible. To share my love of Python and Network Science, I have presented workshops at multiple conferences like PyCon US, SciPy US, PyData London and many more!
This workshop is for data scientists and other programmers who want to add another tool in their data science toolkit. Modelling, analysing and visualising data as networks! Network Science deals with analysing network data, and the data can come from different fields like politics, finance, computer science, law and even Game of Thrones!
We all know and love our carefully designed CI pipelines, which tests our code and makes sure by adding some code or fixing a bug we aren’t introducing a regression in the codebase. But we often don’t give the same treatment to benchmarking as we give to correctness. The benchmarking tests are usually one off scripts written to test a specific change. In this talk, we will discuss various strategies to test our code for performance regressions using ASV (airspeed velocity) for python projects.